How to use ForkJoinPool to use multiple cores in java?

Question

So I am trying to understand about how ForkJoinPool works. I am trying to achieve better performance using this for a large array of about 2 million elements and then adding their reciprocal. I understand that ForkJoinPool.commpnPool().invoke(task); calls compute() which forks the task in two tasks if it is not smaller and then computes and then joins them. So far, we are using two cores.

But if I want to xecute this on multiple cores, how do I do that and achieve 4 times better performance than the usual single thread run? Below is my code for default ForkJoinPool():

@Override
        protected void compute() {
            // TODO
            if (endIndexExclusive - startIndexInclusive <= seq_count) {
                for (int i = startIndexInclusive; i < endIndexExclusive; i++)
                    value += 1 / input[i];
            } else {

                ReciprocalArraySumTask left = new ReciprocalArraySumTask(startIndexInclusive,
                        (endIndexExclusive + startIndexInclusive) / 2, input);
                ReciprocalArraySumTask right = new ReciprocalArraySumTask((endIndexExclusive + startIndexInclusive) / 2,
                        endIndexExclusive, input);
                left.fork();
                right.compute();
                left.join();
                value = left.value + right.value;
            }
        }
    }


protected static double parArraySum(final double[] input) {
        assert input.length % 2 == 0;

        double sum = 0;

        // Compute sum of reciprocals of array elements
        ReciprocalArraySumTask task = new ReciprocalArraySumTask(0, input.length, input);
        ForkJoinPool.commonPool().invoke(task);
        return task.getValue();
    }

//Here I am trying to achieve with 4 cores
protected static double parManyTaskArraySum(final double[] input,
                                                final int numTasks) {
        double sum = 0;
        System.out.println("Total tasks = " + numTasks);
        System.setProperty("java.util.concurrent.ForkJoinPool.common.parallelism", String.valueOf(numTasks));
        // Compute sum of reciprocals of array elements
        int chunkSize = ReciprocalArraySum.getChunkSize(numTasks, input.length);
        System.out.println("Chunk size = " + chunkSize);
        ReciprocalArraySumTask task = new ReciprocalArraySumTask(0, input.length, input);
        ForkJoinPool pool = new ForkJoinPool();
//        pool.
        ForkJoinPool.commonPool().invoke(task);
        return task.getValue();
    }

Answer 1

This is my approach:

• Threshold is the limit when the compute starts to calculate and stops to stack recursive calls, this works better if each processor is used twice or more (there is a limit of course), that's because I use numTask * 2.

   protected static double parManyTaskArraySum(final double[] input,
                                           final int numTasks) {
       int start;
       int end;
  
       int size = input.length;
       int threshold = size / (numTasks * 2);
  
       List<ReciprocalArraySumTask> actions = new ArrayList<>();
  
       for (int i = 0; i < numTasks; i++) {
           start = getChunkStartInclusive(i, numTasks, size);
           end = getChunkEndExclusive(i, numTasks, size);
           actions.add(new ReciprocalArraySumTask(start, end, input, threshold, I));
       }
       ForkJoinTask.invokeAll(actions);
  
       return actions.stream().map(ReciprocalArraySumTask::getValue).reduce(new Double(0), Double::sum);
  
    }
  

Answer 2

You want to use 4 cores but you are giving a job which will need only two cores. In the following example, getChunkStartInclusive and getChunkEndExclusive methods give the range for beginning and ending indexes of each chunk. I believe the following code can solve your problem and give you some implementation idea.

protected static double parManyTaskArraySum(final double[] input,
        final int numTasks) {
    double sum = 0;
    System.setProperty("java.util.concurrent.ForkJoinPool.common.parallelism", String.valueOf(numTasks));
    List<ReciprocalArraySumTask> ts = new ArrayList<ReciprocalArraySumTask>(numTasks);

    int i;
    for (i = 0; i < numTasks - 1 ; i++) {
        ts.add(new ReciprocalArraySumTask(getChunkStartInclusive(i,numTasks,input.length),getChunkEndExclusive(i,numTasks,input.length),input));
        ts.get(i).fork();
    }
    ts.add( new ReciprocalArraySumTask(getChunkStartInclusive(i, numTasks, input.length), getChunkEndExclusive(i, numTasks, input.length), input));
    ts.get(i).compute();

    for (int j = 0; j < numTasks - 1; j++) {
        ts.get(j).join();
    }

    for (int j = 0; j < numTasks; j++) {
        sum += ts.get(j).getValue();
    }
    return sum;
}